| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Outline of Final Research Achievements |
In this research project, we developed a topological language-based clustering technique that can flexibly grasp the structure of data by converting the concept of persistent homology, which plays a central role in topological data analysis, into language-based clustering and incorporating it into language-based clustering. Furthermore, we aimed at the theoretical development of topological language-based clustering and its practical application to mining social data by examining the mathematical relationships between conventional clustering techniques and development techniques.
We have obtained some results on the development of language-based clustering algorithms and clustering algorithms that incorporate persistent homology. Further study on making linguistic rules for persistent homology is a future topic.
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